The control system of a driverless vehicle relies on sensors to sense the surrounding environment by means of a sensor. In order to obtain more comprehensive information, the driverless vehicle is usually equipped with a variety of sensor devices. Different sensor devices may be complementary to each other, for providing more comprehensive information to the control system of the driverless vehicle. Types of the sensors usually configured in the driverless vehicle comprise a LIDAR sensor, a camera sensor, a GPS (Global Positioning System), etc.
The variety of sensors can provide the control system of the driverless vehicle with more comprehensive information, but also bring certain complexity. Data from different sensors need to undergo a multi-aspect and multi-level merge process including synchronization, correlation, combination, and the like, to reduce redundant information, and to improve the environment identification accuracy of the control system. In the prior art, the data collected by a plurality of LIDAR sensors is aligningly stored by means of a time synchronizer, and when time stamps of point cloud output data of the plurality of LIDAR sensors are the same, the data is combined and output.
However, the existing driverless vehicle is often equipped with different types of sensors, different types of sensors have different coverage ranges and work cycles, and the manner of using the time synchronizer may cause information losses and timing errors, so that there may be a problem that the data alignment of multiple types of sensors is not supported.